A Deep learning based streamlit web app which can tell with which bollywood celebrity your face resembles.

Overview

Project Name: Which Bollywood Celebrity You look like

A Deep learning based streamlit web app which can tell with which bollywood celebrity your face resembles.

Face recognition is the problem of identifying and verifying people in a photograph by their face. It is a task that is trivially performed by humans, even under varying light and when faces are changed by age or obstructed with accessories and facial hair. Nevertheless, it is remained a challenging computer vision problem for decades until recently.

Deep learning methods are able to leverage very large datasets of faces and learn rich and compact representations of faces, allowing modern models to first perform as-well and later to outperform the face recognition capabilities of humans.

In this project, you will discover the problem of face recognition and how deep learning methods can achieve superhuman performance to identify similar faces.

Original repo:

https://github.com/entbappy/Which-Bollywood-Celebrity-You-look-like

Demo Video:

https://www.youtube.com/watch?v=lYSsLFgFzBk&list=PLkz_y24mlSJYI78C1IZJaghNvC7dh6red&index=6

This is a methods of identifying similar faces check various aspects on pictures, including: face shape, nose, eyes and mouth; face position in the picture; skin color (including the lighting of the photo); color and hair and cosine_similarity.

Dataset has beem used:

https://www.kaggle.com/sushilyadav1998/bollywood-celeb-localized-face-dataset

Some Real Time Demo:

Web app look

workflow

Lets check some of images

workflow

workflow

This really performing good you can consider by seeing this result 😀

workflow

The fun part is I am looked like Riteish Deshmukh 😄 😁

workflow

STEPS to run this project:

You can also use others images instead of bollywood actress

STEP 01:

Clone the repository

git clone https://github.com/entbappy/Which-Bollywood-Celebrity-You-look-like.git

STEP 02:

Create an environment

conda create -n celebrity python=3.7 -y

STEP 03:

Install the requirements

pip install -r requirements.txt

STEP 04:

Download the data from the link and keep it in your project directory. Make sure all the actress folder should be in just one folder called data, like that

workflow

STEP 05:

Just execute this command one time if you are not changing the data

python run.py

STEP 06:

Now to start the webapp run the following command

streamlit run app.py

yes!! Now you can start predicting 🙂

Authors:

Author: Bappy Ahmed
Data Scientist
Email: [email protected]
Owner
BAPPY AHMED
A Data Scientist by Profession 🙂
BAPPY AHMED
TCPNet - Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition

Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition This is an implementation of TCPNet. Introduction For video recognition task, a g

Zilin Gao 21 Dec 08, 2022
A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

Xcessiv Xcessiv is a tool to help you create the biggest, craziest, and most excessive stacked ensembles you can think of. Stacked ensembles are simpl

Reiichiro Nakano 1.3k Nov 17, 2022
Neural implicit reconstruction experiments for the Vector Neuron paper

Neural Implicit Reconstruction with Vector Neurons This repository contains code for the neural implicit reconstruction experiments in the paper Vecto

Congyue Deng 35 Jan 02, 2023
TUPÃ was developed to analyze electric field properties in molecular simulations

TUPÃ: Electric field analyses for molecular simulations What is TUPÃ? TUPÃ (pronounced as tu-pan) is a python algorithm that employs MDAnalysis engine

Marcelo D. Polêto 10 Jul 17, 2022
Cupytorch - A small framework mimics PyTorch using CuPy or NumPy

CuPyTorch CuPyTorch是一个小型PyTorch,名字来源于: 不同于已有的几个使用NumPy实现PyTorch的开源项目,本项目通过CuPy支持

Xingkai Yu 23 Aug 17, 2022
Augmented CLIP - Training simple models to predict CLIP image embeddings from text embeddings, and vice versa.

Train aug_clip against laion400m-embeddings found here: https://laion.ai/laion-400-open-dataset/ - note that this used the base ViT-B/32 CLIP model. S

Peter Baylies 55 Sep 13, 2022
On the model-based stochastic value gradient for continuous reinforcement learning

On the model-based stochastic value gradient for continuous reinforcement learning This repository is by Brandon Amos, Samuel Stanton, Denis Yarats, a

Facebook Research 46 Dec 15, 2022
Weakly Supervised Segmentation by Tensorflow.

Weakly Supervised Segmentation by Tensorflow. Implements semantic segmentation in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).

CHENG-YOU LU 52 Dec 27, 2022
Audio Visual Emotion Recognition using TDA

Audio Visual Emotion Recognition using TDA RAVDESS database with two datasets analyzed: Video and Audio dataset: Audio-Dataset: https://www.kaggle.com

Combinatorial Image Analysis research group 3 May 11, 2022
Source code of NeurIPS 2021 Paper ''Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration''

CaGCN This repo is for source code of NeurIPS 2021 paper "Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration". Paper L

6 Dec 19, 2022
The Submission for SIMMC 2.0 Challenge 2021

The Submission for SIMMC 2.0 Challenge 2021 challenge website Requirements python 3.8.8 pytorch 1.8.1 transformers 4.8.2 apex for multi-gpu nltk Prepr

5 Jul 26, 2022
Source code for the paper: Variance-Aware Machine Translation Test Sets (NeurIPS 2021 Datasets and Benchmarks Track)

Variance-Aware-MT-Test-Sets Variance-Aware Machine Translation Test Sets License See LICENSE. We follow the data licensing plan as the same as the WMT

NLP2CT Lab, University of Macau 5 Dec 21, 2021
The self-supervised goal reaching benchmark introduced in Discovering and Achieving Goals via World Models

Lexa-Benchmark Codebase for the self-supervised goal reaching benchmark introduced in 'Discovering and Achieving Goals via World Models'. Setup Create

1 Oct 14, 2021
An SE(3)-invariant autoencoder for generating the periodic structure of materials

Crystal Diffusion Variational AutoEncoder This software implementes Crystal Diffusion Variational AutoEncoder (CDVAE), which generates the periodic st

Tian Xie 94 Dec 10, 2022
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks.

FDRL-PC-Dyspan Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks. This repository contains the entire code

Peyman Tehrani 17 Nov 18, 2022
Efficiently Disentangle Causal Representations

Efficiently Disentangle Causal Representations Install dependency pip install -r requirements.txt Main experiments Causality direction prediction cd

4 Apr 01, 2022
Sound Event Detection with FilterAugment

Sound Event Detection with FilterAugment Official implementation of Heavily Augmented Sound Event Detection utilizing Weak Predictions (DCASE2021 Chal

43 Aug 28, 2022
NALSM: Neuron-Astrocyte Liquid State Machine

NALSM: Neuron-Astrocyte Liquid State Machine This package is a Tensorflow implementation of the Neuron-Astrocyte Liquid State Machine (NALSM) that int

Computational Brain Lab 4 Nov 28, 2022
Code for ACM MM 2020 paper "NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination"

NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination The offical implementation for the "NOH-NMS: Improving Pedestrian Detection by

Tencent YouTu Research 64 Nov 11, 2022
Machine learning for NeuroImaging in Python

nilearn Nilearn enables approachable and versatile analyses of brain volumes. It provides statistical and machine-learning tools, with instructive doc

919 Dec 25, 2022